Exploring daily wind data using the Meteostat Python Library
Group Members: Travis, Ira, Micah
Course: Data Science – Phase 1
Goal: Explore and compare wind trends in distinct U.S. regions
Source: Meteostat Python API
Dataset Type: Aggregated weather observations per station
Key Variableswspd: Average wind speed (km/h)wdir: Mean wind direction (degrees)tavg: Average air temperature (°C)coco: Condition codeTime Period: 2024
Locations: ~30
Frame: Hourly, Daily, Monthly with a focus on Hourly
Units: Metric (km/h, degrees, °C)
| Region | Station (City) | Station ID | Climate Context |
|---|---|---|---|
| Florida | Miami Intl Airport | 72202 | Hurricane-prone coastal region |
| Oklahoma | Oklahoma City | 72353 | Tornado Alley with frequent severe winds |
| Pennsylvania | Pittsburgh Intl Airport | 72520 | Inland relative known temperature |
| California | Los Angeles Intl Airport | 72295 | Pacific coastal winds and mountain effects |
How do wind patterns change by region?
What are some case studies of extreme weather?
How do geograhical feature (lakes, oceans, mountains, deserts, plains) impact?